The widespread adoption of e-commerce methods has enabled sophisticated, real-time use of sales information in marketing goods to consumers, and in managing the logistics of supplies, inventory, and shipping. In connection with marketing goods, for example, it is known to advise the potential buyer of a product, who is viewing the product in a browser window, of related goods bought by other customers who have made the same purchase. Users of Amazon.com®, for example, are familiar with the routine display of products “frequently bought together” with the product under consideration, and with the display “Customers Who Bought This Item Also Bought”, followed by a list of products purchased at one time or another by buyers of the product under consideration.
It is also known to monitor a consumer's regular purchase of consumables, and to maintain a shopping list individualized for that consumer. Users of Drugstore.com®, for example, are familiar with the Your List′ display of frequently-purchased consumables. It is also known to send reminders to the consumer, via email, that replenishment of regularly purchased goods is likely due, along with a convenient link to the merchant's web site.
Such methods are limited to data mining of past purchase histories of the consumer, and of other consumers who have considered or purchased identical or similar products. Data mining of the latter sort tends to reflect mass tastes and preferences, and is not individualized to the targeted consumer's tastes and preferences. There remains a need for systems and methods that enable consumers to discover new products that might not be linked in a database of past purchases. The present invention meets this need via a learning algorithm which constantly updates explicit indicators of individual consumer preferences, and which also infers likely consumer preferences from indicators of the consumer's social context and browsing and reading habits, as well as the purchase history.